Industry 4.0 in Aerospace: Building the Foundation for AI

Aerospace leaders are already talking about Industry 5.0, physical AI, autonomy, and highly adaptive factories. At the same time, many manufacturers are still building the connected data, modern processes, and digital systems that define Industry 4.0 in aerospace.

That gap was clear at a recent Wichita Aero Club luncheon sponsored by Akkodis in partnership with ISG. After an ISG market trends presentation, the panel returned to one practical message.

Companies don’t need to wait for every emerging technology to mature, but they do need foundations that will allow those technologies to work when they arrive. 

Industry 4.0 in Aerospace Is Still the Immediate Priority

Industry 4.0 is the connected factory floor, where sensors, systems, operational data, and automation work together. Industry 5.0 builds on that base with more adaptive automation and closer human-machine collaboration, so it assumes the earlier foundation is already in place.

ISG’s aerospace research presented at the luncheon described a connected engineering-to-manufacturing data foundation as a business requirement. It also highlighted fragmentation across CAD and CAE systems, PLM, MES, ERP, suppliers, and MRO data.

For many organizations, aerospace digital transformation needs to focus there first. Clean data, consistent work instructions, connected production systems, and clear quality controls may sound less exciting than autonomous factories, but they’re what make advanced AI usable at scale.

Akkodis’ work in Industry 4.0 and manufacturing operations follows the same progression across industrial automation, connected manufacturing systems, IoT, and analytics.

Smart Manufacturing Starts with Usable Data and Repeatable Processes

A practical progression starts by standardizing core operations, connecting key signals, and improving control of exceptions.

For an aerospace manufacturer, that can mean:

• Standardizing MES templates, quality gates, and work instructions.
• Connecting telemetry, material readiness, and labor information so constraints surface earlier.
• Creating clearer workflows for exceptions, quality containment, and blocked work.
• Using root-cause analytics to improve processes based on reliable operational data.

The same principle applies whether the organization is operating a major production campus or a smaller supplier facility. Smart manufacturing becomes easier to build when the underlying processes are consistent enough for technology to support them.

Shreyans Shrimal, Sales Director at Akkodis, described the shift in practical terms:

Traditional aerospace factories were built around a specific aircraft program and often remained largely unchanged for decades. Software-defined manufacturing transforms that model: the factory adapts to changing aircraft configurations, production volumes, and certification requirements—instead of forcing every product through a fixed process. That flexibility, delivered without compromising quality, traceability, or safety is the true disruptor advantage and a design philosophy every aerospace manufacturer can begin adopting today

Where AI in Aerospace Manufacturing Is Delivering Value

For legacy manufacturers and smaller suppliers, useful AI starting points are often problems they already understand well. The goal isn’t to automate everything at once, but to apply AI where the process, data, and outcome are clear.

Quality Inspection as a Practical First Step

Manual visual inspection remains essential in aerospace, but it can be time-intensive and difficult to scale. Computer-vision inspection and smart factory quality control are areas where AI is supporting faster inspection cycles, fewer missed defects, and less rework.

AI-assisted defect detection can become a practical first investment when stable inspection criteria and usable image data are already available. Akkodis has explored the same approach through its work in AI-assisted computer vision for manufacturing.

Predictive Maintenance Protects Production Time

Unplanned downtime matters even more when a smaller supplier relies on a limited number of CNC machines, presses, or specialized tools. Predictive maintenance uses machine and sensor data to identify signs of deterioration before equipment failure interrupts production. 

Predictive maintenance is a current aerospace AI use case because it can improve availability and maintenance planning. Reliable equipment data remains the prerequisite, since disconnected machines and inconsistent records leave AI with too little context. 

Knowledge Capture Protects Expertise

ISG estimates that 40% of critical manufacturing knowledge is at risk from the SME retirement wave over the next five years.

AI-assisted knowledge capture and AR/VR tools can help preserve how experienced machinists and engineers diagnose problems, make adjustments, and handle exceptions before that expertise leaves the organization. The value comes from making specialized knowledge easier to retain and transfer, rather than expecting technology to replace the judgment behind it.

Aerospace Digital Transformation Needs More Than New Technology

The recurring lesson from our discussion with business leaders in Wichita was that AI in aerospace manufacturing depends on the operating environment around it. A model cannot fix unclear process ownership, fragmented data, inconsistent work instructions, or production systems that cannot share information.

Manufacturers standardize how work is done, connect operational signals, improve control over exceptions, and then build analytics that support continuous improvement. AI becomes more useful at each stage because it has cleaner inputs and a clearer role in the process.

Akkodis’ broader Aerospace and Defense, AI, Data and Analytics, and aerospace digital engineering work spans those same boundaries between engineering, manufacturing, and digital technology.

Building the Foundation for What Comes Next

The aerospace industry doesn’t need to choose between finishing Industry 4.0 and preparing for Industry 5.0. Improving data quality, connecting production systems, standardizing critical processes, and investing in clear AI use cases are practical steps that make more advanced automation possible later.

ISG has recognized Akkodis as a leader in both Engineering Digitalization and Innovation and Technology Transformation and Consulting, reinforcing the combination of aerospace and digital engineering expertise behind these conversations. 

For organizations deciding where to strengthen their Industry 4.0 foundation or where AI can create practical value on the shop floor, our team can help assess the starting point and identify next steps that fit the operation.

Click here to talk to our experts about your aerospace manufacturing priorities today.